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Sampling in Marketing Research
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Basics of sampling I
A sample is apart of a whole
to show what the
rest is like.
Sampling helps to
determine the
corresponding
value of the
population and
plays a vital role in
marketing
research.
Samples offer many benefits: Save costs:Less expensive to study the
sample than the population.
Save time:Less time needed to study the
sample than the population .
Accuracy:Since sampling is done with
care and studies are conducted by skilled
and qualified interviewers, the results are
expected to be accurate.
Destructive nature of elements:For someelements, sampling is the way to test, since
tests destroy the element itself.
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Basics of sampling II
Limitations of Sampling
Demands more rigid control
in undertaking sample
operation.
Minority and smallness in
number of sub-groups oftenrender study to be
suspected.
Accuracy level may be
affected when data is
subjected to weighing. Sample results are good
approximations at best.
Sampling Process
Defining thepopulation
Developinga sampling
Frame
DeterminingSample
Size
SpecifyingSampleMethod
SELECTING THE SAMPLE
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Sampling: Step 1
Defining the Universe
Universe or population is thewhole mass under study.
How to define a universe:
What constitutes the units of
analysis (HDB apartments)?
What are the sampling units
(HDB apartments occupied in
the last three months)?
What is the specific designation
of the units to be covered (HDB
in town area)? What time period does the data
refer to (December 31, 1995)
Sampling: Step 2
Establishing the SamplingFrame
A sample frame is the list of all
elements in the population
(such as telephone directories,
electoral registers, club
membership etc.) from whichthe samples are drawn.
A sample frame which does not
fully represent an intended
population will result inframe
error and affect the degree of
reliability of sample result.
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Step - 3
Determination of Sample Size
Sample size may be determined by using:
Subjective methods (less sophisticated methods)
The rule of thumb approach: eg. 5% of population
Conventional approach: eg. Average of sample sizes ofsimilar other studies;
Cost basis approach: The number that can be studied
with the available funds;
Statistical formulae (more sophisticated methods)Confidence interval approach.
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Conventional approach of Sample size determination using
Sample sizes used in different marketing research studies
TYPE OF STUDY MINIMUM
SIZE
TYPICAL
RANGE
dentifying a problem (e.g.market
segmentation) 500 1000-2500
roblem-solving (e.g., promotion) 200 300-500
roduct tests 200 300-500
dvertising (TV, Radio, or print Media
per commercial or ad tested) 150 200-300
est marketing 200 300-500
est market audits 10stores/outlets
10-20stores/outlets
ocus groups 2 groups 4-12 groups
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Sample size determination using statistical formulae:
The confidence interval approach
To determine sample sizes using statistical formulae,
researchers use the confidence interval approach based on the
following factors:
Desired level of data precision or accuracy;
Amount of variability in the population (homogeneity); Level of confidence required in the estimates of population values.
Availability of resources such as money, manpower and time
may prompt the researcher to modify the computed sample
size. Students are encouraged to consult any standard marketing
research textbook to have an understanding of these formulae.
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Step 4:
Specifying the sampling method
Probability Sampling
Every element in the target population or universe [sampling
frame] has equal probability of being chosen in the sample for
the survey being conducted.
Scientific, operationally convenient and simple in theory. Results may be generalized.
Non-Probability Sampling
Every element in the universe [sampling frame] does not have
equal probability of being chosen in the sample.
Operationally convenient and simple in theory.
Results may not be generalized.
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Probability sampling
Appropriate for
homogeneous population
Simple random sampling
Requires the use of a random
number table.
Systematic sampling
Requires the sample frame
only,
No random number table isnecessary
Appropriate for
heterogeneous population
Stratified sampling
Use of random number
table may be necessary
Cluster sampling
Use of random number
table may be necessary
Four types of probability sampling
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Non-probability sampling
Four types of non-probability samplingtechniques
Very simple types, based on subjective criteria
Convenient sampling
Judgmental sampling
More systematic and formal
Quota sampling
Special type
Snowball Sampling
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Simple Random Sampling
Also called random
sampling
Simplest method of
probability
sampling
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 37 75 10 49 98 66 03 86 34 80 98 44 22 22 45 83 53 86 23 51
2 50 91 56 41 52 82 98 11 57 96 27 10 27 16 35 34 47 01 36 08
3 99 14 23 50 21 01 03 25 79 07 80 54 55 41 12 15 15 03 68 56
4 70 72 01 00 33 25 19 16 23 58 03 78 47 43 77 88 15 02 55 67
5 18 46 06 49 47 32 58 08 75 29 63 66 89 09 22 35 97 74 30 80
6 65 76 34 11 33 60 95 03 53 72 06 78 28 14 51 78 76 45 26 45
7 83 76 95 25 70 60 13 32 52 11 87 38 49 01 82 84 99 02 64 00
8 58 90 07 84 20 98 57 93 36 65 10 71 83 93 42 46 34 61 44 01
9 54 74 67 11 15 78 21 96 43 14 11 22 74 17 02 54 51 78 76 76
10 56 81 92 73 40 07 20 05 26 63 57 86 48 51 59 15 46 09 75 64
11 34 99 06 21 22 38 22 32 85 26 37 00 62 27 74 46 02 61 59 81
12 02 26 92 27 95 87 59 38 18 30 95 38 36 78 23 20 19 65 48 5013 43 04 25 36 00 45 73 80 02 61 31 10 06 72 39 02 00 47 06 98
14 92 56 51 22 11 06 86 88 77 86 59 57 66 13 82 33 97 21 31 61
15 67 42 43 26 20 60 84 18 68 48 85 00 00 48 35 48 57 63 38 84
Need to use
Random
Number Table
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How to Use Random Number Tables
_______________________________________________1. Assign a unique number to each population element in the
sampling frame. Start with serial number 1, or 01, or 001,
etc. upwards depending on the number of digits required.
2. Choose a random starting position.
3. Select serial numbers systematically across rows or downcolumns.
4. Discard numbers that are not assigned to any population
element and ignore numbers that have already been
selected.
5. Repeat the selection process until the required number ofsample elements is selected.
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How to Use a Table of Random Numbers to Select a Sample
Your marketing research lecturer wants to randomly select 20 students from
your class of 100 students. Here is how he can do it using a random number table.Step 1: Assign all the 100 members of the population a unique number.You may
identify each element by assigning a two-digit number. Assign 01 to the first name
on the list, and 00 to the last name. If this is done, then the task of selecting the
sample will be easier as you would be able to use a 2-digit random number table.
NAME NUMBER NAME NUMBER
Adam, Tan 01 Tan Teck Wah 42
Carrol, Chan 08 Tay Thiam Soon 61
. .. Jerry Lewis 18 Teo Tai Meng 87
. .
Lim Chin Nam 26 . Yeo Teck Lan 99
Singh, Arun 30 Zailani bt Samat 00
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Step 2: Select any starting point in the Random Number Table and find the first number that
corresponds to a number on the list of your population. In the example below, # 08 has been
chosen as the starting point and the first student chosen is Carol Chan.
10 09 73 25 33 76
37 54 20 48 05 64
08 42 26 89 53 19
90 01 90 25 29 09
12 80 79 99 70 8066 06 57 47 17 34
31 06 01 08 05 45
Step 3: Move to the next number, 42 and select the person corresponding to that number into
the sample. #87 Tan Teck Wah
Step 4: Continue to the next number that qualifies and select that person into the sample.
# 26 -- Jerry Lewis, followed by #89, #53 and #19Step 5: After you have selected the student # 19, go to the next line and choose #90. Continue
in the same manner until the full sample is selected. If you encounter a number selected
earlier (e.g., 90, 06 in this example) simply skip over it and choose the next number.
Starting point:move right to the end
of the row, then downto the next row row;move left to the end,then down to the next
row, and so on.
How to use random number table to select a random sample
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Systematic sampling
Very similar to simple random sampling with one exception.
In systematic sampling only one random number is needed throughout the
entire sampling process.
To use systematic sampling, a researcher needs:
[i] a sampling frame of the population; and is needed.
[ii] a skip interval calculated as follows:
Skip interval = population list size
Sample size
Names are selected using the skip interval.
If a researcher were to select a sample of 1000 people using the local telephone
directory containing 215,000 listings as the sampling frame, skip interval is
[215,000/1000], or 215. The researcher can select every 215th name of the entire
directory [samplingframe], and select his sample.
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Example: How to Take a Systematic Sample
Step 1: Select a listing of the population, say the City Telephone Directory, from which to
sample. Remember that the list will have an acceptable level of sample frame error.
Step 2: Compute the skip interval by dividing the number of entries in the directory by thedesired sample size.
Example: 250,000 names in the phone book, desired a sample size of 2500,
So skip interval = every 100th name
Step 3: Using random number(s), determine a starting position for sampling the list.
Example: Select: Random number for page number. (page 01)
Select: Random number of column on that page. (col. 03)
Select: Random number for name position in that column (#38, say, A..Mahadeva)Step 4: Apply the skip interval to determine which names on the list will be in the sample.
Example: A. Mahadeva (Skip 100 names), new name chosen is A Rahman b Ahmad.
Step 5: Consider the list as circular; that is, the first name on the list is now the initial name
you selected, and the last name is now the name just prior to the initially selected one.
Example: When you come to the end of the phone book names (Zs), just continue on
through the beginning (As).
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Stratified sampling I
A three-stage process:
Step 1- Divide the population into
homogeneous, mutually exclusive
and collectively exhaustive subgroups
or strata using some stratification
variable;
Step 2- Select an independent simplerandom sample from each stratum.
Step 3- Form the final sample by
consolidating all sample elements
chosen in step 2.
May yield smaller standard errors ofestimators than does the simple random
sampling. Thus precision can be gained
with smaller sample sizes.
Stratified samples can be:
Proportionate: involving the
selection of sample elements
from each stratum, such that
the ratio of sample elements
from each stratum to the
sample size equals that of thepopulation elements within
each stratum to the total
number of population
elements.
Disproportionate: the sample
is disproportionate when the
above mentioned ratio is
unequal.
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To select a proportionate stratified sample of 20 members of the Island Video Club which has
100 members belonging to three language based groups of viewers i.e., English (E), Mandarin
(M) and Others (X).
Step 1: Identify each member from the membership list by his or her respective language groups00 (E ) 20 (M) 40 (E ) 60 ( X ) 80 (M)
01 (E ) 21 ( X ) 41 ( X ) 61 (M) 81 (E )
02 ( X ) 22 (E ) 42 ( X ) 62 (M) 82 (E )
03 (E ) 23 ( X ) 43 (E ) 63 (E ) 83 (M)
04 (E ) 24 (E ) 44 (M) 64 (E ) 84 ( X )
05 (E ) 25 (M) 45 (E ) 65 ( X ) 85 (E )
06 (M) 26 (E ) 46 ( X ) 66 (M) 86 (E )
07 (M) 27 (M) 47 (M) 67 (E ) 87 (M)08 (E ) 28 ( X ) 48 (E ) 68 (M) 88 ( X )
09 (E ) 29 (E ) 49 (E ) 69 (E ) 89 (E )
10 (M) 30 (E ) 50 (E ) 70 (E ) 90 ( X )
11 (E ) 31 (E ) 51 (M) 71 (E ) 91 (E )
12 ( X ) 32 (E ) 52 ( X ) 72 (M) 92 (M)
13 (M) 33 (M) 53 (M) 73 (E ) 93 (E )
14 (E ) 34 (E ) 54 (E ) 74 ( X ) 94 (E )
15 (M) 35 (M) 55 (E ) 75 (E ) 95 ( X )16 (E ) 36 (E ) 56 (M) 76 (E ) 96 (E )
17 ( X ) 37 (E ) 57 (E ) 77 (M) 97 (E )
18 ( X ) 38 ( X ) 58 (M) 78 (M) 98 (M)
19 (M) 39 ( X ) 59 (M) 79 (E ) 99 (E )
Selection of a proportionate Stratified Sample
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Step 2: Sub-divide the club members into three homogeneous sub-groups or strata by the
language groups: English, Mandarin and others.
EnglishLanguage Mandarin Language Other LanguageStratum Stratum Stratum .
00 22 40 64 82 06 35 66 02 42
01 24 43 67 85 07 44 68 12 46
03 26 45 69 86 10 47 72 17 52
04 29 48 70 89 13 51 77 18 60
05 30 49 71 91 15 53 78 21 65
08 31 50 73 93 19 56 80 23 7409 32 54 75 94 20 58 83 28 84
11 34 55 76 96 25 59 87 38 88
14 36 57 79 97 27 61 92 39 90
16 37 63 81 99 33 62 98 41 95
1.Calculate the overall sampling fraction, f, in the following manner:
f = n = 20 = 1 =N 100 5
where n = sample size and N = population size
0.2
Selection of a proportionate stratified sample II
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Determine the number of sample elements (n1) to be selected from the English
language stratum. In this example, n1 = 50 x f = 50 x 0.2 =10. By using a simplerandom sampling method [using a random number table] members whose numbers
are 01, 03, 16, 30, 43, 48, 50, 54, 55, 75, are selected.
Next, determine the number of sample elements (n2) from the Mandarin language
stratum. In this example, n2 = 30 x f = 30 X 0.2 = 6. By using a simple random
sampling method as before, members having numbers 10,15, 27, 51, 59, 87 areselected from the Mandarin language stratum.
In the same manner, the number of sample elements (n3) from the Other language
stratum is calculated. In this example, n3 = 20 x f = 20 X 0.2 = 4. For this stratum,
members whose numbers are 17, 18, 28, 38 are selected
These three different sets of numbers are now aggregated to obtain the ultimate
stratified sample as shown below.
S = (01, 03, 10, 15, 16, 17, 18, 27, 28, 30, 38, 43, 48, 50, 51, 54, 55, 59, 75, 87)
Selection of a proportionate stratified sample III
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Cluster sampling
Is a type of sampling in which clusters or groups of
elements are sampled at the same time.
Such a procedure is economic, and it retains the
characteristics of probability sampling.
A two-step-process: Step 1- Defined population is divided into number of mutually
exclusive and collectively exhaustive subgroups or clusters;
Step 2- Select an independent simple random sample of clusters.
One special type of cluster sampling is called area sampling, where
pieces of geographical areas are selected.
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Example : One-stage and two-stage Cluster sampling
Consider the same Island Video Club example involving 100 club members:
Step 1: Sub-divide the club members into 5 clusters, each cluster containing 20 members.
Cluster
No. English Mandarin Others
1 00, 22, 40, 64, 82 06, 35, 66 02, 42
01, 24, 43, 67, 85 07, 44, 68 12, 46
2 03, 26, 45, 69, 86 10, 47, 72 17, 52
04, 29, 48, 70, 89 13, 51, 77 18, 60
3 05, 30, 49, 71, 91 15, 53, 78 21, 65
08, 31, 50, 73, 93 19, 56, 80 23, 744 09, 32, 54, 75, 94 20, 58, 83 28, 84
11, 34, 55, 76, 96 25, 59, 87 38, 88
5 14, 36, 57, 79, 97 27, 61, 92 39, 90
16, 37, 63, 81, 99 33, 62, 98 41, 95
Step 2: Select one of the 5 clusters. If cluster 4 is selected, then all its elements (i.e. Club
Members with numbers 09, 11, 32, 34, 54, 55, 75, 76, 94, 96, 20, 25, 58, 59, 83, 87, 28, 38, 84,
88) are selected.
Step 3: If a two-stage cluster sampling is desired, the researcher may randomly select 4 members
from each of the five clusters. In this case, the sample will be different from that shown in step 2
above.
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Stratified Sampling vs Cluster Sampling
Stratified Sampling Cluster Sampling1.The target population is sub-divided
into a few subgroups or strata, each
containing a large number of elements.
1.The target population is sub-
divided into a large number of
sub-population or clusters, each
containing a few elements.
2.Within each stratum, the elements are
homogeneous. However, high degree ofheterogeneity exists between strata.
2.Within each cluster, the elements
are heterogeneous. Betweenclusters, there is a high degree of
homogeneity.
3.A sample element is selected each time. 3.A cluster is selected each time.
4.Less sampling error. 4.More prone to sampling error.
5.Objective is to increase precision. 5.Objective is to increase samplingefficiency by decreasing cost.
AREA SAMPLING
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AREA SAMPLING
A common form of cluster sampling where clusters consist of geographic areas, such as
districts, housing blocks or townships. Area sampling could be one-stage, two-stage, or
multi-stage.
How to Take an Area Sample Using Subdivisions
Your company wants to conduct a survey on the expected patronage of its new outlet in a new
housing estate. The company wants to use area sampling to select the sample households to be
interviewed. The sample may be drawn in the manner outlined below.
___________________________________________________________________________________
Step 1: Determine the geographic area to be surveyed, and identify its subdivisions. Each
subdivision cluster should be highly similar to all others. For example, choose ten housing
blocks within 2 kilometers of the proposed site [say, Model Town ] for your new retail outlet;
assign each a number.
Step 2: Decide on the use of one-step or two-step cluster sampling. Assume that you decide to
use a two-stage cluster sampling.
Step 3: Using random numbers, select the housing blocks to be sampled. Here, you select 4
blocks randomly, say numbers #102, #104, #106, and #108.Step 4: Using some probability method of sample selection, select the households in each of the
chosen housing block to be included in the sample. Identify a random starting point (say,
apartment no. 103), instruct field workers to drop off the survey at every fifth house
(systematic sampling).
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Non-probability samples
Convenience sampling
Drawn at the convenience of the researcher. Common in exploratory research.
Does not lead to any conclusion.
Judgmental sampling
Sampling based on some judgment, gut-feelings or experience of the researcher.
Common in commercial marketing research projects. If inference drawing is not
necessary, these samples are quite useful. Quota sampling
An extension of judgmental sampling. It is something like a two-stage judgmental
sampling. Quite difficult to draw.
Snowball sampling
Used in studies involving respondents who are rare to find. To start with, the
researcher compiles a short list of sample units from various sources. Each of
these respondents are contacted to provide names of other probable respondents.
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Quota Sampling
To select a quota sample comprising 3000 persons in country X using three control
characteristics: sex, age and level of education.
Here, the three control characteristics are considered independently of one another.
In order to calculate the desired number of sample elements possessing the various
attributes of the specified control characteristics, the distribution pattern of the
general population in country X in terms of each control characteristics is examined.
Control
Characteristics Population Distribution Sample Elements .
Gender: .... Male...................... 50.7% Male 3000 x 50.7% = 1521
................. Female .................. 49.3% Female 3000 x 49.3% = 1479
Age: ......... 20-29 years ........... 13.4% 20-29 years 3000 x 13.4% = 402
................. 30-39 years ........... 53.3% 30-39 years 3000 x 52.3% = 1569
................. 40 years & over .... 33.3% 40 years & over 3000 x 34.3% = 1029
Religion: .. Christianity ........... 76.4% Christianity 3000 x 76.4% = 2292
................. Islam ..................... 14.8% Islam 3000 x 14.8% = 444
................. Hinduism .............. 6.6% Hinduism 3000 x 6.6% = 198
................. Others ................... 2.2% Others 3000 x 2.2% = 66
__________________________________________________________________________________
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Sampling vs non-sampling errors
Sampling Error [SE] Non-sampling Error [NSE]
Very small sampleSize
Larger sample size
Still larger sample
Complete census
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Choosing probability vs. non-probability sampling
Probability Evaluation Criteria Non-probability
sampling samplingConclusive Nature of research Exploratory
Larger sampling Relative magnitude Larger non-sampling
errors sampling vs. error
non-sampling error
High Population variability Low
[Heterogeneous] [Homogeneous]
Favorable Statistical Considerations Unfavorable
High Sophistication Needed Low
Relatively Longer Time Relatively shorter
High Budget Needed Low
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Sampling Examples I
Topic: A Comparative empirical studybetween Public & Private life insurancecompanies
Population: Public & Private life insuranceholders
Sample: Sample of 100 selected using
Judgment & convenient method where 76belongs to LIC and 24 Private sector
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Example II
Topic: Challenges faced by working womenin Bangladesh A Study on Khulna City
Population: Working women in Khulna City(Public & Pvt sector banks, Insurance,MNCs, NGOs, Govt Organizations)
Sampling Method: Stratified Random
Sample ( Proportional allocation)
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Banks
40
Govt Org
10
NGOs
15
MNCS
15
Others
20
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Example III
Topic: Performance Management in RetailSector in India An Empirical Study
Population: Employees of Retail Industry ofIndore city
Source List: Big Bazaar, Pantaloons,Reliance Fresh, West side, Treasure Island
Sample Size: 104 front line employees areselected using Simple Random Sample
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Example IV
Topic: Effect of e-CRM on BusinessOpportunities: A Study with reference to smalland medium scale Enterprises in India
Population: Consumers of SME Location: Bangalore ( Major SME Locations
identified and from each location one SME isconsidered for the study. Homogeneity in size,
structure, other demographic & organizationalfactors are considered in selection)
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Further Criterion; SMEs must posses a website and a valid email Id, because, this isminimum technological infrastructure to
implement e-CRM.
Sample Size: From each SME 10consumers are selected at random.
Total size: 10 * 10 = 100
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Example V
Topic: The Production & Labour problems ofsmall scale Entrepreneurs
Location: Madurai Region of Tamil Nadu
Source: Tamil Nadu Small IndustriesDevelopment Corporation (TNSIDCO) Manual& Web site
Sample: Divided Madurai region into 5Industrial estates. Stratified Random Sampling
(Proportional allocation) method is followed inselecting sample.
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Region Population Sample
Kappalur 169 56
Andipatti 05 02Theni 37 12
Pudur 74 25
Uranganpatti 147 49Total 432 144